The present invention generally relates to medical image analysis. In particular, the present invention relates to use of image segmentation in computer-aided medical image analysis and diagnosis.
Due to complexity of medical image segmentation and speed and accuracy requirements of a clinical environment, clinical image segmentation is a challenging topic in computer-aided medical image analysis and diagnosis.
Segmentation is used, among other things, to distinguish objects from background. Techniques, such as threshold techniques, edge-based methods, region-based techniques, and connectivity-preserving relaxation methods, may be used for image segmentation.
Threshold techniques make decisions based on local pixel information. Threshold techniques may be effective when intensity levels fall outside a range of levels in the background. Because spatial information is ignored, however, blurred region boundaries can create havoc for image segmentation.
Edge-based methods center around contour detection. However, edge-based methods exhibit weakness in connecting broken contour lines and thus may be prone to failure in the presence of blurring.
A region-based method typically involves partitioning an image into connected regions by grouping neighboring pixels of similar intensity levels. Adjacent regions are then merged under some criterion involving perhaps homogeneity or sharpness of region boundaries. Overly stringent merging criteria may create fragmentation; lenient criterion may overlook blurred boundaries and result in over merging.
Triple-region segmentation relates to segmentation of images that naturally consist of three distinct regions of interest. Triple-region segmentation finds wide application in medical imaging due to the fact that most X-ray, computed tomography (CT), magnetic resonance (MR) and ultrasound images can be modeled as a triple-region segmentation problem.
Level set methods are a versatile technique for medical image segmentation due to an ability to capture a topology of shapes in medical imagery. However, although some work has been reported on medical image segmentation using level set, many challenges remain in the application of level set for medical image segmentation, especially clinical image segmentation, even for triple-region images. Multiple level set functions may be used consecutively or simultaneously to segment triple-region medical images. However, these approaches are time consuming and/or suffer from convergence problems.
Current level sets are not suitable for clinical segmentation. Not only are current level set function techniques time consuming due to complicated medical structure, but level set functions are also sensitive to placement of an initial contour. Therefore, the running time of current level set methods heavily relies on the position and size of the initial curves and the complexity of objects, for example.
Current level set functions are restricted to the separation of two regions. As soon as more than two regions are considered, current level set solutions lose much of their attractiveness. Only a few current solutions focus on level set based segmentation for more than two regions, which can be divided into the following two categories: coupled level set and hierarchical level set. These techniques are of limited use and have significant limitations as described below.
Coupled level set employs multiple level set functions simultaneously for multi-region segmentation. Using coupled level set, one level set function is assigned to each of the multiple regions to be segmented. The number of level set functions is equal to the number of regions in the image. This technique, however, assumes an initially fixed number of regions. Alternatively, the number of regions may be estimated in a preliminary stage by means of a Gaussian mixture estimate of an n image histogram. This way, the number of mixture coefficients determines the number of regions. In a different approach, level set functions are used in such a way that N regions are represented by only log2N level set functions. Unfortunately, this approach results in empty regions if log2N is a floating point number rather than an integer. These empty regions have undefined statistics, though the statistics still appear in evolution equations. To segment a triple-region image using coupled level set method, three coupled level set functions have typically been used. While coupled level set functions have attempted to segment triple regions, coupled level set solutions suffer from slow convergence and local minimization problems. For example, coupled level set functions do not converge for some initial curves, and, for some cases, different initial contours may give different segmented results.
Hierarchical level set segmentation employs an hierarchical approach to extend a two region segmentation method to multiple region segmentation. An image is first segmented into two regions by one level set function. Then, based on a variance analysis of each region, the program decides to further segment one or both regions. The procedure is done recursively until the whole image is properly segmented. Although compared to coupled level set, hierarchical level set has advantage through easier implementation and faster segmentation, for triple-region segmentation, it requires use of two level set segmentations consecutively. Therefore, the processing time involved is at least doubled.